Overview of Time-Domain and Spectral-Domain Indices
Many biological systems have inherent oscillatory patterns. An example of such an inherent oscillatory pattern is heart rate (HR) variability which is the variation in the time interval between successive beats of the heart. This also may be referred to as respiratory arrhythmia because the periodic slowing and acceleration of HR is synchronous with respiration. A similar oscillatory pattern is seen in blood pressure (BP). Analysis of the oscillatory pattern in the time-domain results in characterizing the oscillatory signal in general statistical terms; for example, HR may be characterized as mean +/− standard deviation (SD). Of note, oftentimes instead of HR (beats/min), the interval between heart beats (R-R interval, in milliseconds) is reported and HR variability (HRV) is expressed as the variability between successive R-R intervals (i.e., as ‘R-R variability’). Various prior art methods for assessing HRV are shown in FIG. 3. Unless otherwise stated, the variability in the ECG is expressed as R-R variability in the present disclosure (FIG. 1A).
In addition to the time-domain analysis, an oscillatory signal can be analyzed in the frequency-domain making use of a technique such as Fourier transformation or it can be analyzed according to chaos theory. The oscillatory signal is described as a sum of a series of sinusoidal and cosinusoidal functions of various amplitudes and frequencies to determine an instantaneous-amplitude spectrum. The spectrum, termed the autopower spectral density (APSD), describes the oscillatory signal in terms of the oscillatory power present in each frequency interval and the area under the APSD curve corresponds to the amount or amplitude of each specific fluctuation frequency in the original oscillatory signal. The APSD is calculated by determining the normalized power of a signal from the instantaneous amplitude-spectrum in accordance with the equation (1) below:Gaa=ave(SaSa*)/df  (1)
where Gaa=instantaneous amplitude spectral density of a sampling channel (“a”); Sa=instantaneous amplitude spectrum of channel a; Sa*=complex conjugate of Sa; df=frequency resolution. For a more detailed discussion of the use of Fourier transformation to describe oscillatory biological signals, the reader may consult Stout, et al., Anaesthetic Pharmacology and Physiology Review volume 4, issue 1 pages 96-110, 1996 and U.S. Pat. No. 4,862,361 issued to Gordon, et al. A typical APSD is shown in FIG. 1B.
To perform these analyses for the ECG, one must first determine the precise time of each beat (i.e., of each R-wave). Each R-wave of the ECG is typically identified by the combination of derivative plus threshold detection of the fiducial point of data sampled at 250 Hz (250 times/sec). The successive HR values or R-R intervals are used to generate the ‘HR tachogram’ or ‘R-R tachogram,’ respectively, with time on the x-axis and HR or R-R interval on the y-axis. Since the HR and R-R tachograms are not really continuous waveforms (they are generated by sampling the HR or R-R for each beat at 5 Hz), the tachogram is more aptly described as pseudocontinuous. It has discrete, variable-interval data that are converted to a waveform. Thus, the prior art also describes spectral-domain analysis of the ECG in terms of variable-interval, beat-to-beat data, wherein a single data point is plotted at the time of each beat (as opposed to at 5 Hz). This procedure has not been considered to be necessary for treatment of continuous waveforms such as continuous flow and continuous BP. Thus discrete, variable-interval data of these indices has not been utilized for spectral-domain analysis. This invention will include an explanation of the need for performing these steps and a methodology for utilizing discrete, variable-interval data to perform spectral analysis of continuous waveforms.
Autonomic Nervous System
The autonomic nervous system controls many key processes including the activity of cardiac muscle, smooth muscles and glands. It is divided into the parasympathetic nervous system and the sympathetic nervous system. The sympathetic, or adrenergic, nervous system innervates the major organs such as the heart (where it causes increased contractility and increased HR) and blood vessels (where it typically causes vasoconstriction by causing contraction of vascular smooth muscle cells). The parasympathetic, or cholinergic, nervous system also innervates the major organs, such as the heart, where it causes decreased contractility and decreased HR. Prior to the present invention, the parasympathetic nervous system was believed to have minimal effect on the peripheral vasculature.
Oscillatory activities controlled by the branches of the autonomic nervous system will have an inherent frequency that is dependent upon which branch of the autonomic nervous system controls the activity. Oscillatory activity controlled by the sympathetic nervous system is characterized by low frequency (LF) oscillations of less than about 0.12 Hz. The present invention additionally recognizes that oscillatory activity controlled by the parasympathetic nervous system is characterized not only by this low frequency, but also by high frequency (HF) activity as fast as 0.5 Hz. Based on this, the present invention, as described below, is able to determine the presence or absence of sympathetic or parasympathetic activity in an oscillatory signal by analyzing the APSD of the oscillatory signal and determining its power in certain frequency ranges. (see, e.g., FIG. 6 discussed below).
HR variability has both a parasympathetic component and a sympathetic component and thus has characteristic frequency components (FIG. 1B). Analysis of the presence, absence, or quantity of each of these components, i. e., of power in a particular region of the APSD, has been correlated to prognosis in a variety of pathological conditions. A reduction in parasympathetic activity (reduction in high-frequency signal) has been correlated to arrhythmias and a poor prognosis in congestive heart failure (Frey, et al., J Am Coll Cardiol 212:286A, 1993). A similar reduction in parasympathetic activity as evidenced by a reduction in power in the HF region of the APSDR-R has been correlated to a poor prognosis in autonomic neuropathy associated with diabetes (Bernardi, et al., Acta Diabetol Lat. 23:141-54, 1986). In hypertensive subjects, a relatively decreased level of parasympathetic activity in HR variability as may be seen at rest or in response to a sympathomimetic challenge is seen (Furlan, et al., J Hypertens 5:S423-5, 1987). Moreover, declines in the effects of parasympathetic activity on the ECG may precede clinical evidence of hypertension (Markovitz J H, Matthews K A, Kannel W B, Cobb J L: Psychological predictors of hypertension in the Framingham study: is there tension in hypertension? JAMA 1993; 270(20): 2439-2494; Langewitz W, Ruddel H, Schachinger H: Reduced parasympathetic cardiac control in patients with hypertension at rest and under mental stress. Am Heart J 1994;127:1228-8) and diabetic autonomic neuropathy (Van Ravenswaaij-Arts CMA, Kollée L A A, Hopman J C W, Stoelinga G B A, van Geijn P: Heart rate variability. Ann Intern Med 1993; 118:436-47; Hosking D J, Bennett T, Hampton J R: Diabetic autonomic neuropathy. Diabetes 1978;27: 1043-55; Ewing D J, Campbell I W, Clarke B F: Assessment of cardiovascular effects in diabetic autonomic neuropathy and prognostic implications. Ann Intern Med 1980; 92(part 2):308-11; Bellavere F, Bosello G, Cardone C, Girardello L, Ferri M, Fedele D: Evidence of early impairment of parasympathetic reflexes in insulin dependent diabetics without autonomic symptoms. Diabete Metab 1985; 11:152-6; Pfeifer M A, Cook D, Brodsky J, Tice D, Reenan A, Swedine S, et al.: Quantitative evaluation of cardiac parasympathetic activity in normal and diabetic man. Diabetes 1982;31:339-45; Eckberg D L, Harkins S W, Fritsch J M, Musgrave G E, Gardner D F: Baroreflex control of plasma norepinephrine and heart period in healthy subjects and diabetic patients. J Clin Invest 1986;78:366-374; Duchen L W, Anjorin A, Watkins P J, Mackay J D: Pathology of autonomic neuropathy in diabetes mellitus. Ann Intern Med 1980;92:301-3; Kitney R I, Byrne S, Edmonds M E, Watkins P J, Roberts V C: Heart rate variability in the assessment of autonomic diabetic neuropathy. Automedica 1982;4:155-67; Freeman R, Saul J P, Roberts M S, Berger R D, Broadbridge C, Cohen R J: Spectral analysis of heart rate in diabetic neuropathy. Arch Neurol 1991;48:185-190; Pagani M, Malfatto G, Pierini S, Casati R, Masu A M, Poli M, Guzzetti S, Lombardi F, Cerutti S, Malliani A: Spectral analysis of heart rate variability in the assessment of autonomic diabetic neuropathy. J Auton Nerv Syst 1988;23:143-153; Malliani A, Pagani M, Lombardi F, Cerutti S: Cardiovascular neural regulation explored in the frequency domain. Circ 84:482-489, 1991) as well as associated organ injury. Loss of HF oscillatory activity was associated with poor wound healing in diabetic patients (van den Akker T J, Koeleman A S, Hogenhuis L A, Rompelman O: Heart rate variability and blood pressure oscillations in diabetics with autonomic neuropathy. Automedica 1983;4:201-8) and was the major resistive factor in hypertensive rats (Borders J L: Vasomotion patterns in skeletal muscle in normal and hypertensive rats. Abstract of Dissertation, 1980, Univ. of CA, Berger R D, Saul J P, Cohen R J: Transfer function analysis of autonomic regulation. I. Canine atrial rate response. Am J Physiol 256:H142-H152, 1989). Both conditions are associated with decreased parasympathetic activity (Ewing 1985, Kitney 1982, Paganai JANS 1988; Langewitz, 1994; Guzzetti S, Piccaluga E, Casati R, Cerutti S, Lombardi F, Pagani M, Malliani A: Sympathetic predominance in essential hypertension: a study employing spectral analysis of heart rate variability. J Hypertension 1988; 6:711-7), but this was not addressed by those investigators.
Assessment of Oscillations
There are multiple ways to assess oscillations in the peripheral vasculature, including laser Doppler flowmetry (LDF). LDF is a technique for assessing arteriolar and capillary blood flow at the level of the microvasculature. In LDF, laser light at a wavelength absorbed and reflected by hemoglobin is directed onto a tissue such as the skin of the subject and penetrates the surface of the tissue. The light contacts red blood cells and is reflected by moving blood cells; this causes the laser light to undergo a Doppler shift. The flux of the red blood cells through the blood vessels (concentration times velocity) can be calculated by measuring the wavelength shift of the reflected light. Continuous monitoring of the LDF signal delineates the pulsatile changes throughout the course of each heart beat and the superimposed oscillations induced by autonomic activity.
Prior to the present invention, the parasympathetic nervous system was believed to have only a minor role in peripheral vasoregulation and “virtually no effect on peripheral resistance” (Guyton A C, Hall J E: Nervous regulation of the circulation, and rapid control of arterial pressure. In: Guyton A C, Hall J E: Textbook of Medical Physiology, Ninth Edition. WB Saunders Co. Philadelphia, 1996, pp. 209-220; ch. 18). Hence, no one sought to measure oscillations in the peripheral microvasculature as a means of monitoring microvascular cholinergic activity. Instead, HF oscillations in the microvasculature simply were attributed to transmission of atropine-sensitive HF oscillations at the heart (e.g., respiration-induced, cholinergically mediated variations in HR and BP (Pomeranz B, Macaulay R J B, Caudill M A, Kutz I, Adam D, Gordon D, Kilbom K M, Barger A C, Shannon D C, Cohen R J, Benson H: Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 248:H151-H153, 1985; Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzinelli P, Sandrone G, Malfatto G, Dell'Orto S, Piccaluga E, Turiel M, Baselli G, Cerutti S, Malliani A: Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res 59:178-193, 1986; Lossius K, Eriksen M: Spontaneous flow waves detected by laser Doppler in human skin. Microvasc Research 50:94-104, 1995; Akselrod S, Gordon D, Madwed J B, Snidman N C, Shannon D C, Cohen R J: Hemodynamic regulation: investigation by spectral analysis. Am J Physiol 1985; 249:H867-H875; Bernardi L, Hayoz D, Wenzel R, Passino C, Calciati A, Weber R, Noll G: Synchronous and baroreceptor-sensitive oscillations in skin microcirculation: evidence for central autonomic control. Am J Physiol 273:H 1867-H1878, 1997) herein termed COCHR) to relatively passive vascular beds. (Hertzman A B, Roth L W: The absence of vasoconstrictor reflexes in the forehead circulation. Effects of cold. Am J Physiol 136:692-697, 1942; Lossius K, 1995; Bernardi L, 1997).